Shared Data Intelligence-Driven Multi-Task Prediction for Efficient Resource Utilization in 6G Networks

Abstract

The emergence of AI-native 6G networks necessitates efficient and proactive resource management mechanisms to support highly dynamic and data-intensive services. Existing approaches typically employ independent prediction models for mobility, handover, and channel quality, leading to redundant data processing and limited exploitation of cross-layer dependencies. In this paper, we propose a shared data intelligence–driven multi-task prediction framework that jointly models mobility, handover, and channel quality indicator (CQI) within a unified learning architecture. By leveraging a common feature space and a single processing pipeline, the proposed framework simultaneously generates multiple correlated predictions, thereby reducing computational overhead. A correlation analysis using real-world datasets demonstrates that mobility, CQI, and handover events exhibit inherent inter-dependencies, justifying the use of a shared representation. Furthermore, a processing time comparison shows that the proposed approach achieves approximately 10.12% reduction compared to conventional independent prediction models by eliminating redundant feature extraction and repeated model execution. These results validate that shared data intelligence is an effective and scalable solution for efficient multi-task prediction in real-time 6G network environments.